"privacy" entries

#NoEstimates — Allspaw also points out that the yearning to break the bonds of estimation is nothing new — he’s fond of quoting a passage from The Unwritten Laws of Engineering, a 1944 manual which says that engineers “habitually try to dodge the irksome responsibility for making commitments.” All of Allspaw’s segment is genius.

Old Fashioned Snapchat — get a few drinks in any brand advertiser and they’ll admit that the number one reason they know that brand advertising works is that, if they stop, sales inevitably drop.

GPG and Me (Moxie Marlinspike) — Even though GPG has been around for almost 20 years, there are only ~50,000 keys in the “strong set,” and less than 4 million keys have ever been published to the SKS keyserver pool ever. By today’s standards, that’s a shockingly small user base for a month of activity, much less 20 years. This was a great talk at Webstock this year.

Remotely Bricking Cars (BoingBoing) — story from 2010 where an intruder illegally accessed Texas Auto Center’s Web-based remote vehicle immobilization system and one by one began turning off their customers’ cars throughout the city.

Machine Learning Classification over Encrypted Data (PDF) — It is worth mentioning that our work on privacy-preserving classification is complementary to work on differential privacy in the machine learning community. Our work aims to hide each user’s input data to the classification phase, whereas differential privacy seeks to construct classifiers/models from sensitive user training data that leak a bounded amount of information about each individual in the training data set. See also The Morning Paper’s unpacking of it.

Privacy of Phone Audio (Reddit) — unconfirmed report from Redditor I started a new job today with Walk N’Talk Technologies. I get to listen to sound bites and rate how the text matches up with what is said in an audio clip and give feed back on what should be improved. At first, I though these sound bites were completely random. Then I began to notice a pattern. Soon, I realized that I was hearing peoples commands given to their mobile devices. Guys, I’m telling you, if you’ve said it to your phone, it’s been recorded…and there’s a damn good chance a 3rd party is going to hear it.

The digital world has been colonized by a dangerous idea: that we can and should solve problems by preventing computer owners from deciding how their computers should behave. I’m not talking about a computer that’s designed to say, “Are you sure?” when you do something unexpected — not even one that asks, “Are you really, really sure?” when you click “OK.” I’m talking about a computer designed to say, “I CAN’T LET YOU DO THAT DAVE” when you tell it to give you root, to let you modify the OS or the filesystem.

Case in point: the cell-phone “kill switch” laws in California and Minneapolis, which require manufacturers to design phones so that carriers or manufacturers can push an over-the-air update that bricks the phone without any user intervention, designed to deter cell-phone thieves. Early data suggests that the law is effective in preventing this kind of crime, but at a high and largely needless (and ill-considered) price.

To understand this price, we need to talk about what “security” is, from the perspective of a mobile device user: it’s a whole basket of risks, including the physical threat of violence from muggers; the financial cost of replacing a lost device; the opportunity cost of setting up a new device; and the threats to your privacy, finances, employment, and physical safety from having your data compromised. Read more…

In 2011, Kashmir Hill, Gizmodo and others alerted us to a privacy gaffe made by Fitbit, a company that makes small devices to help people keep track of their fitness activities. It turns out that Fitbit broadcast the sexual activity of quite a few of their users. Realizing this might not sit well with those users, Fitbit took swift action to remove the search hits, the data, and the identities of those affected. Fitbit, like many other companies, believed that all the data they gathered should be public by default. Oops.

Does anyone think this is the last time such a thing will happen?

Fitness data qualifies as “personal,” but sexual data is clearly in the realm of the “intimate.” It might seem like semantics, but the difference is likely to be felt by people in varying degrees. The theory of contextual integrity says that we feel violations of our privacy when informational contexts are unexpectedly or undesirably crossed. Publicizing my latest workout: good. Publicizing when I’m in flagrante delicto: bad. This episode neatly exemplifies how devices are entering spaces where they’ve not tread before, physically and informationally. Read more…

Security is at the heart of the web.

At the end of the day, though, we want to be able to go to sleep without worrying that all of those great conversations on the open web will endanger the rest of what we do.

Making the web work has always been a balancing act between enabling and forbidding, remembering and forgetting, and public and private. Managing identity, security, and privacy has always been complicated, both because of the challenges in each of those pieces and the tensions among them.

Complicating things further, the web has succeeded in large part because people — myself included — have been willing to lock their paranoias away so long as nothing too terrible happened.

I talked for years about expecting that the NSA was reading all my correspondence, but finding out that yes, indeed they were filtering pretty much everything, opened the door to a whole new set of conversations and concerns about what happens to my information. I made my home address readily available in an IETF RFC document years ago​. In an age of doxxing and SWATting, I wonder whether I was smart to do that. As the costs move from my imagination to reality, it’s harder to keep the door to my paranoia closed. Read more…

As devices become more intelligent and networked, the makers and vendors of those devices gain access to greater amounts of personal data. In the extreme case of the washing machine, the kind of data — who uses cold versus warm water — is of little importance. But when the device collects biophysical information, location data, movement patterns, and other sensitive information, data collectors have both greater risk and responsibility in safeguarding it. The advantages of every company becoming a software company — enhanced customer analytics, streamlined processes, improved view of resources and impact — will be accompanied by new privacy challenges.

A key question emerges from the increasing intelligence of and monitoring by devices: will the commercial practices that evolved in the web be transferred to the Internet of Things? The amount of control users have over data about them is limited. The ubiquitous end-user license agreement tells people what will and won’t happen to their data, but there is little choice. In most situations, you can either consent to have your data used or you can take a hike. We do not get to pick and choose how our data is used, except in some blunt cases where you can opt out of certain activities (which is often a condition forced by regulators). If you don’t like how your data will be used, you can simply elect not to use the service. But what of the emerging world of ubiquitous sensors and physical devices? Will such a take-it-or-leave it attitude prevail? Read more…

3D-Printing Carbon Fibre (Makezine) — the machine doesn’t produce angular, stealth fighter-esque pieces with the telltale CF pattern seen on racing bikes and souped up Mustangs. Instead, it creates an FDM 3D print out of nylon filament (rather than ABS or PLA), and during the process it layers in a thin strip of carbon fiber, melted into place from carbon fiber fabric using a second extruder head. (It can also add in kevlar or fiberglass.)

Building the Workplace We Want (Slack) — culture is the manifestation of what your company values. What you reward, who you hire, how work is done, how decisions are made — all of these things are representations of the things you value and the culture you’ve wittingly or unwittingly created. Nice (in the sense of small, elegant) explanation of what they value at Slack.

The Internet of Things Has Four Big Data Problems (Alistair Croll) — What the IoT needs is data. Big data and the IoT are two sides of the same coin. The IoT collects data from myriad sensors; that data is classified, organized, and used to make automated decisions; and the IoT, in turn, acts on it. It’s precisely this ever-accelerating feedback loop that makes the coin as a whole so compelling. Nowhere are the IoT’s data problems more obvious than with that darling of the connected tomorrow known as the wearable. Yet, few people seem to want to discuss these problems.

Keysweeper — a stealthy Arduino-based device, camouflaged as a functioning USB wall charger, that wirelessly and passively sniffs, decrypts, logs, and reports back (over GSM) all keystrokes from any Microsoft wireless keyboard in the vicinity. Designs and demo videos included.

Google’s Philosopher — interesting take on privacy. Now that the mining and manipulation of personal information has spread to almost all aspects of life, for instance, one of the most common such questions is, “Who owns your data?” According to Floridi, it’s a misguided query. Your personal information, he argues, should be considered as much a part of you as, say, your left arm. “Anything done to your information,” he has written, “is done to you, not to your belongings.” Identity theft and invasions of privacy thus become more akin to kidnapping than stealing or trespassing. Informational privacy is “a fundamental and inalienable right,” he argues, one that can’t be overridden by concerns about national security, say, or public safety. “Any society (even a utopian one) in which no informational privacy is possible,” he has written, “is one in which no personal identity can be maintained.”

S-1 for a Bitcoin Trust (SEC) — always interesting to read through the risks list to see what’s there and what’s not.

Computationally Modelling Human Emotion (ACM) — our work seeks to create true synergies between computational and psychological approaches to understanding emotion. We are not satisfied simply to show our models “fit” human data but rather seek to show they are generative in the sense of producing new insights or novel predictions that can inform understanding. From this perspective, computational models are simply theories, albeit more concrete ones that afford a level of hypothesis generation and experimentation difficult to achieve through traditional theories.

Opinion Formation Models on a Gradient (PLoSONE) — Many opinion formation models embedded in two-dimensional space have only one stable solution, namely complete consensus, in particular when they implement deterministic rules. In reality, however, deterministic social behavior and perfect agreement are rare – at least one small village of indomitable Gauls always holds out against the Romans. […] In this article we tackle the open question: can opinion dynamics, with or without a stochastic element, fundamentally alter percolation properties such as the clusters’ fractal dimensions or the cluster size distribution? We show that in many cases we retrieve the scaling laws of independent percolation. Moreover, we also give one example where a slight change of the dynamic rules leads to a radically different scaling behavior.

Clustering Bitcoin Accounts Using Heuristics (O’Reilly Radar) — In theory, a user can go by many different pseudonyms. If that user is careful and keeps the activity of those different pseudonyms separate, completely distinct from one another, then they can really maintain a level of, maybe not anonymity, but again, cryptographically it’s called pseudo-anonymity. […] It turns out in reality, though, the way most users and services are using bitcoin, was really not following any of the guidelines that you would need to follow in order to achieve this notion of pseudo-anonymity. So, basically, what we were able to do is develop certain heuristics for clustering together different public keys, or different pseudonyms.

A Primer on Hardware Security: Models, Methods, and Metrics (PDF) — Camouflaging: This is a layout-level technique to hamper image-processing-based extraction of gate-level netlist. In one embodiment of camouflaging, the layouts of standard cells are designed to look alike, resulting in incorrect extraction of the netlist. The layout of nand cell and the layout of nor cell look different and hence their functionality can be extracted. However, the layout of a camouflaged nand cell and the layout of camouflaged nor cell can be made to look identical and hence an attacker cannot unambiguously extract their functionality.

Prompter: A Domain-Specific Language for Versu (PDF) — literally a scripting language (you write theatrical-style scripts, characters, dialogues, and events) for an inference engine that lets you talk to characters and have a different story play out each time.